A Probabilistic Framework for Dynamic Object Recognition in 3D Environment With A Novel Continuous Ground Estimation Method

TL;DR

This paper introduces a probabilistic framework for dynamic 3D object recognition that includes a novel Gaussian Process Regression method for accurate ground detection in complex urban environments, improving robustness and performance.

Contribution

The paper presents a new GPR-based ground detection method and integrates it into a probabilistic framework for dynamic object recognition in 3D scenes, with implementation in ROS.

Findings

Outperforms existing GPR-based ground segmentation methods in uneven scenes

Effectively detects ground in sloped and rough urban environments

Demonstrates robustness and accuracy in simulation results

Abstract

In this thesis a probabilistic framework is developed and proposed for Dynamic Object Recognition in 3D Environments. A software package is developed using C++ and Python in ROS that performs the detection and tracking task. Furthermore, a novel Gaussian Process Regression (GPR) based method is developed to detect ground points in different urban scenarios of regular, sloped and rough. The ground surface behavior is assumed to only demonstrate local input-dependent smoothness. kernel's length-scales are obtained. Bayesian inference is implemented sing \textit{Maximum a Posteriori} criterion. The log-marginal likelihood function is assumed to be a multi-task objective function, to represent a whole-frame unbiased view of the ground at each frame because adjacent segments may not have similar ground structure in an uneven scene while having shared hyper-parameter values. Simulation results shows the effectiveness of the proposed method in uneven and rough scenes which outperforms similar Gaussian process based ground segmentation methods.